Systematic modeling of architecture design spaces is needed when architecting complex systems, to support experts in making less biased decisions, and to formulate the optimization problem needed to explore the large combinatorial design space. Existing methods do not offer enough compatibility with the Model-Based Systems Engineering (MBSE) approaches, cannot model all needed design scenarios, or are not flexible enough when it comes to architecture evaluation. A new method is presented that provides a semantic representation of the architecture design space, modeled as the Architecture Design Space Graph (ADSG). The ADSG represents three types of architectural decisions: function-component mapping, component characterization, and component connection. The ADSG is constructed from a design space definition, and discrete architectural decisions are automatically inserted according to specified rules. Once decisions and metrics have been defined, the hierarchical, mixed-integer, multi-objective optimization problem can be formulated: decisions are mapped to design variables, and performance metrics are mapped to objectives or constraints. An application of the method to the Apollo mission architecting problem is presented. Nomenclature

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The research and innovation AGILE project has developed an approach, the socalled AGILE Paradigm, focusing on the acceleration of the deployment and operation of collaborative Multidisciplinary Design Analysis Optimization systems, which in turns can be exploited to accelerate the development of complex products, such as novel aerospace systems. Although the technologies developed for the implementation of the paradigm, have proved to reduce the deployment and operational time to more than 40% with respect to conventional MDAO approaches, the AGILE Paradigm has not been formalized and model by digital design engineering practices. This work introduces a novel approach leveraging MBSE principles to streamline the development of agile MDAO design systems, and establishing a bridge between MBSE and MDAO. Major outcomes here presented are the MBSE-driven models of the so-called AGILE MDAO system, representing the architecture, the requirements, as well as the organizational aspects, and all the interactions and activities implemented during the life-cycle stages of the MDAO system. The MBSE Architectural Framework, which defines the underlying ontological concepts and perspectives driving the development of the AGILE MDAO system model, are modeled and presented as well. The paper introduces for the first time the overall approach, as well as the high-level elements of the models developed, here represented by making use of SysML standard. The described approach is at the core of the recently launched project AGILE4.0, in which its scope will be expanded to cover the entire life-cycle of the development of complex aeronautical systems.

The AGILE project is developing the next generation of development processes, and deploying a collaborative MDO design system, called the AGILE development framework (ADF). Naturally, such a system contains a lot of implicit assumptions on how things should be done and how to exploit different existing technologies. This collection of assumptions and technologies is labeled the 'AGILE Paradigm'. The two main building blocks of this paradigm are the Collaborative Architecture and the Knowledge Architecture. In essence, these building blocks aim to support large, heterogeneous teams of experts in performing collaborative development in a streamlined and time-effective way. This paper has a focus on the definition of the Knowledge Architecture (KA) as a general conceptual framework which is independent of the aircraft-specific application in AGILE. The KA can be applied to perform collaborative automated design in large, heterogeneous teams for any complex system (e.g. aircraft, automobiles, wind farms). The KA is structured with a multi-level backbone: Development Process layer, Automated Design layer, Design Competence layer. A fourth transverse layer impacting all other layers is the Data & Schemas layer. Each layer has its own set of assumptions and technologies, but more importantly, interfaces between the levels have to be created in order to have a fully interconnected development process from each design competence up to the top-level business process. The hierarchical levels and interfaces are described in this paper as a generalized paradigm. In addition, four support platforms of the KA in the AGILE project are described in more detail: the development process environment, graph-based support in the design problem formulation, visualization of large, complex automated design processes, and design concepts formalizations. Finally, a use case from the AGILE project is mapped on this paradigm to demonstrate the use of the KA and its support platforms in a realistic design case.